一种正交反向学习萤火虫算法

针对萤火虫算法求解复杂优化问题时收敛精度较低的问题，该文提出一种正交反向学习策略，嵌入萤火虫算法，得到一种正交反向学习萤火虫算法。正交反向学习策略中，采用重心反向计算，利用群体搜索经验的同时避免搜索依赖坐标；采用正交试验设计，构建部分维上取反向值的正交反向候选解，充分挖掘个体和反向个体在不同维度上的有利信息。在标准测试集上进行验证，实验结果说明了正交反向学习策略的有效性。与多种新近的改进萤火虫算法相比，该算法在大多数函数上获得更高的求解精度。

Orthogonal Opposition Based Firefly Algorithm

Firefly Algorithm (FA) may suffer from the defect of low convergence accuracy depending on the complexity of the optimization problem. To overcome the drawback, a novel learning strategy named Orthogonal Opposition Based Learning (OOBL) is proposed and integrated into FA. In OOBL, first, the opposite is calculated by the centroid opposition, making full use of the population search experience and avoiding depending on the system of coordinates. Second, the orthogonal opposite candidate solutions are constructed by orthogonal experiment design, combining the useful information from the individual and its opposite. The proposed algorithm is tested on the standard benchmark suite and compared with some recently introduced FA variants. The experimental results verify the effectiveness of OOBL and show the outstanding convergence accuracy of the proposed algorithm on most of the test functions.